The purpose of this grant application is to develop a comprehensive analytic framework, the regressive models for the analysis of family data, and to facilitate the transfer of this new biostatistical methodology to cancer genetic epidemiology. Many studies on the genetic epidemiology of cancer are based on families of two or more generations. Such family studies include the assessment of the role of measured risk factors taking into account specific biologic relationships, the determination of the distribution and familial correlations in age-of-onset, segregation linkage and pleiotropy analyses to determine the possible involvement of major genes in the etiology of the cancer(s), and the determination of the applicability of some models of carcinogenesis. The desired statistical framework encompasses dependent binary and survival outcomes with regression variables, but the scope should include modeling the dependence without or with reference to major genes transmitted in families according to the laws of genetics. The analytic framework is the regressive models which account for familial correlations by specifying a regression relationship between a person's phenotype and a set of explanatory variables including his genotype with respect to specific loci, the phenotypes of older relatives, and environmental and life-style covariates. The appeal of this method is that it simultaneously provides for the effects resulting from important gene(s) and those resulting from complex patterns of residual familial correlations, including sib-sib, spouse-spouse, and parent-offspring phenotypic correlations, without or with reference to explicit genetic or environmental causal mechanisms. Known mechanisms of carcinogenesis are incorporated by choosing suitable parametrization. In specific aim I, the formulation of the models is extended to include broadly applicable distributions and methods of inference. These are then adapted in specific aim 2 to do the following: Modeling and analysis of familial aggregation of cancer and risk factors; Distribution and familial correlations in age-of-onset; Segregation analysis; Linkage and pleiotropy analysis. To facilitate the technology transfer (specific aim 3), appropriate software will be developed and training workshops will be organized.